memory.wiki 사업계획 v7 (FINAL)

Your knowledge graph as a URL for any AI.Markdown URL = API for any AI. 사용자가 author. AI가 graph로 정리. 어떤 AI든 URL로 사용.

Brand: memory.wiki (final, 도메인 확보됨) Founder: Hyunsang at Raymind.AI Build started: 2026년 3월 (부업), 2026년 4월 풀타임 Launch deadline: 2026년 8월 말 (16주) Last updated: 2026-05-20

목차

1. Executive Summary

2. v6 → v7 핵심 변경

3. The One Idea

4. Strategic Thesis

5. 3-Tier Architecture (수집/소화/활용)

6. Tier 1: 수집 (Capture)

7. Tier 2: 소화 (Digestion)

8. Tier 3: 활용 (Utilization)

9. Strategic Position

10. Business Model

11. 16-Week Launch Roadmap

12. Launch Strategy

13. 12-Month Outlook

14. Risk Register

15. Brand Identity (TBD)

16. Decisions Log

1. Executive Summary

한 줄로

memory.wiki — your knowledge graph as a URL for any AI.

The one idea (from product itself)

A memory.wiki URL is an API for any AI.

Paste memory.wiki/<id>, memory.wiki/b/<id>, or memory.wiki/hub/<slug> into ChatGPT, Claude, Gemini, or Cursor. LLM이 fetch해서 markdown으로 받음. No app to install. No format to learn. Just markdown over HTTPS.

핵심 차별화

"The graph is the product"

memory.wiki는 모든 surface가 같은 underlying graph를 읽음:

• Concept index (lifelong ontology)

• Concept relations (typed edges)

• Bundle graph_data (themes, insights, edges)

• Embeddings (semantic recall)

→ User가 떠나려면 graph를 잃음. Graph = user의 thinking shape.

Delivery model, not retrieval

• Mem0/Letta: retrieve memory from extraction

• LLM Wiki: retrieve knowledge from local compilation

• memory.wiki: deliver knowledge graph in URL (any AI inherits)

이게 진짜 moat. AI 회사들이 못 copy하는 것.

8월 launch까지 도달 지점

• 3단계 (수집/소화/활용) 각각 핵심 enhancement 1개씩 ship

• Beta 100명 → Public 2,000+ signup

• HN Show HN top 3

• $1,500-2,500 MRR

• 첫 partnership conversations (Cursor, Continue 등)

• Bundle Spec v1.0 RFC 공개

12개월 후

• $80-150K MRR

• 10,000-20,000 paying users

• "AI memory" 카테고리 top 5 인지

• 첫 LLM platform integration

• Bundle Spec community 활성화

2. v6 → v7 핵심 변경

이건 진짜 큰 update예요. 실제 product (mdfy.app/bmZYfZez + mdfy.app/KRKz_MD-) 기반.

변경 1: Brand final commit

v6: mori.wiki 후보 단계 v7: memory.wiki final commit, 도메인 확보됨

변경 2: Strategic thesis sharpen

v6: "Personal knowledge hub for the AI era" v7: "Markdown URL = API for any AI" + "The graph is the product"

doc의 one-idea를 plan thesis로 elevate.

변경 3: 3단계 framework backbone

v6: Three Pillars (Capture / Bundle / Deploy) v7: 수집 / 소화 / 활용 (Capture / Digestion / Utilization)

Hyunsang님이 제안한 framework. 더 자연스럽고 funnel 명확.

변경 4: "Graph"가 핵심 컨셉

v6: Bundle, Hub URL 강조 v7: Graph (concept ontology + relations + embeddings) 강조

The graph is the product. Bundle/Hub는 graph의 view.

변경 5: Launch timing 수정

v6: 2026-06-16 (8주) v7: 2026년 8월 말 (16주, doc 명시)

Sustainable timeline. Quality 시간 확보.

변경 6: Already-built 자산 명시

v6: Multi-surface는 언급 정도 v7: 다음 자산 모두 명시:

• Real-time collaboration (Yjs CRDT)

• 26 MCP tools

• /galaxy 시각화

• Concept ontology auto-extraction

• 3-provider AI failover

• One renderer everywhere

• Compact vs Full payload

변경 7: Rust engine 사실 정정

v6: mdcore (Rust) open source 자랑 v7: 2026-05-16에 sunset, markdown-it (JS) only

Manifesto와 marketing에서 Rust 언급 제거.

변경 8: 도메인 정정

v6: mdfy.cc + /ko/ v7: memory.wiki (단일 도메인, 글로벌)

mdfy.app은 현재 primary, launch 시점에 memory.wiki로 rename.

3. The One Idea

doc에서 직접 가져온 product의 정수:

A memory.wiki URL is an API for any AI.

이게 전부. 다른 모든 feature는 이 URL이 return하는 markdown을 더 useful하게 만드는 것.

3 URL 형태

memory.wiki/<id> — 한 문서

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한 markdown 문서, frontmatter wrapper
원자 단위

memory.wiki/b/<id> — bundle (compact 기본)

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AI graph (themes, insights, edges)
+ Concepts subgraph
+ Documents (link + annotation)
= ~95% 토큰 절약 vs concat

memory.wiki/hub/<slug> — 전체 hub (compact 기본)

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Top-weighted concepts
+ Typed relations
+ Bundle distribution
= 사용자 thinking의 shape

4 moving parts

첫 3개는 LLM이 fetch하는 markdown에 포함됨. Embeddings는 MCP tool (memory_search)을 통해.

4. Strategic Thesis

Thesis 1: Delivery, not retrieval

대부분의 AI memory startup은 retrieval system. RAG, vector DB, semantic search.

memory.wiki는 delivery system. Graph가 URL로 ship됨.

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Retrieval system:
  AI → query → memory store → return chunks

Delivery system:
  AI → fetch URL → receive pre-organized graph

이게 진짜 moat:

• 어떤 AI든 fetch 가능 (no SDK)

• Pre-organized (LLM이 navigation 안 해도 됨)

• ~95% 토큰 효율 (Compact mode)

Thesis 2: The graph is the product

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모든 mdfy surface는 같은 underlying graph의 다른 reader다.
- Galaxy 시각화 = graph의 visual reader
- Bundle digest = graph의 LLM reader
- Hub URL = graph의 navigation reader
- MCP search = graph의 query reader

Graph가 product. Surface는 그래프의 representation.

이게 lock-in mechanism:

• 사용자가 떠나려면 graph를 잃음

• Graph = 사용자의 thinking shape (수개월 누적)

• Export 가능 (open) but reconstruction은 다른 도구로 불가

Thesis 3: Markdown URL = universal AI interface

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Past: AI는 SDK로 접근 (lock-in)
Present: AI는 URL로 접근 (open)
Future: 모든 AI memory는 URL로 deliver

memory.wiki는 이 future를 만드는 시도.

Thesis 4: Two-way LLM relationship

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Pull (URL fetch): LLM이 URL을 GET하고 markdown 받음
   → 모든 AI 도구 (ChatGPT, Claude, Gemini, Perplexity, Cursor)

Push + Pull (MCP): MCP host가 26 tools 호출
   → Claude Desktop, Cursor, Cline, Windsurf

URL이 paste용, MCP가 deep integration용.

Thesis 5: LLM platform default integration (Hyunsang님 vision)

장기 목표:

"ChatGPT, Claude, Cursor가 'memory.wiki 연결하시겠어요?' 물어보고, 사용자가 yes 하면 모든 대화에 자동 컨텍스트."

지금: 사용자가 URL paste 미래: Platform 기본 지원

이 path는:

1. Phase 2-3: 사용자가 manual paste, partnership outreach 시작

2. Phase 4: 첫 partnership 발표

3. Year 2-3: Default integration 위치 차지

5. 3-Tier Architecture

Hyunsang님의 framework. Plan의 backbone.

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┌────────────────────────────────────────┐
│  1. 수집 (Capture)                      │
│  Multi-surface로 markdown 모음          │
│  ↓                                     │
│  Documents at URLs                     │
└────────────────────────────────────────┘
                 ↓
┌────────────────────────────────────────┐
│  2. 소화 (Digestion)                   │
│  AI가 자동으로 graph build              │
│  - Concept extraction                  │
│  - Relations 추출                       │
│  - Bundle analysis                     │
│  - Embeddings                          │
└────────────────────────────────────────┘
                 ↓
┌────────────────────────────────────────┐
│  3. 활용 (Utilization)                 │
│  어떤 AI에든 URL로 deliver              │
│  - Doc URL                             │
│  - Bundle URL (Compact / Full)         │
│  - Hub URL (Compact / Full / Since)    │
│  - MCP integration                     │
└────────────────────────────────────────┘

각 단계가 funnel:

• 수집 = entry (모두 가능)

• 소화 = engagement (사용 누적시 자동)

• 활용 = lock-in (deploy하면 platform 종속)

각 단계가 pricing tier 자연 매핑:

• Free: 수집 unlimited, 소화 limited, 활용 basic

• Pro: 모두 풀 (개인 power user)

• Build: 활용 + API/MCP (AI builder)

• Team: 공유 활용

• Enterprise: self-host

6. Tier 1: 수집 (Capture)

현재 상태 (이미 강함)

✅ Web — mdfy.app (live, Vercel) ✅ Chrome extension — ChatGPT, Claude, Gemini capture ✅ VS Code extension — Marketplace v1.4.0 ✅ Desktop app — DMG v2.2.0, signed + notarized ✅ CLI — npm v1.3.x ✅ MCP server — npm v1.3.x, 26 tools ✅ macOS QuickLook — Bundled in DMG ✅ GitHub import — repo → markdown ✅ Drag-drop, paste-as-markdown

진짜 강점: 다른 도구가 따라잡기 어려운 multi-surface

대부분 memory startup은 web + API만. memory.wiki는 7+ surface가 day one부터.

이게 product의 1st moat.

강화 후보 (모두 keep, launch까지 일부 build)

A. Voice capture ⭐ (Tier 1 priority)

왜 중요: Mobile에 가장 큰 missing piece. 출퇴근, 산책, 회의 후 즉시 사고 capture.

구현:

• iOS app + Android app

• 음성 → Whisper → markdown

• Auto-categorize (concept extract)

• Hub에 자동 추가

Launch 전 build 가능성: Medium (1-2개월). Mobile app은 큰 effort. Launch 후 priority: 매우 높음 (가장 큰 새 surface)

B. iOS Share Sheet / Android Intent ⭐ (Quick win)

왜 중요: 시스템 레벨 통합. 어떤 앱에서든 "Share to memory.wiki".

구현:

• iOS Share Extension

• Android Share Intent

• Twitter thread, blog, AI 답변 → 한 번에 hub

Launch 전 build 가능성: High (2-3주). 빠른 win.

C. AI conversation 자동 capture suggest ⭐

왜 중요: 현재는 사용자가 click. 자동 suggest로 engagement 증가.

구현:

• Chrome extension upgrade

• ChatGPT/Claude 대화 모니터링 (local)

• "Interesting" 답변 detect (heuristics or AI)

• "Save this?" subtle prompt

Launch 전 build 가능성: Medium (3-4주). 가치 매우 큼.

D. Email forward

왜 중요: Viral mechanic. Newsletter, AI 답변 이메일 직접 hub로.

구현:

• forward@memory.wiki (또는 user-specific)

• Email → markdown (HTML 정리)

• Auto-add to hub

• Newsletter sync 기반

Launch 전 build 가능성: Medium (2-3주).

E. Slack/Discord integration

왜 중요: Team tier에 deep. Slack 메시지 react → hub 저장.

구현:

• Slack app

• React with custom emoji → memory.wiki에 save

• Discord bot

• Threads 전체 또는 single message

Launch 전 build 가능성: Low (1개월+). Team feature이라 Phase 3에 적합.

F. RSS/Newsletter sync

왜 중요: 자동 input. Newsletter, blog, podcast description 자동 추가.

구현:

• RSS feed 등록

• New post → hub에 add (draft 또는 auto-publish)

• Filter rules

Launch 전 build 가능성: Medium (2-3주). 매우 niche.

수집 강화 우선순위 (Launch까지)

Launch 전 commit: 1, 2 (iOS Share Sheet + AI auto-suggest). Launch 후 즉시: 3, 4 (Email + Voice). Phase 3: 5, 6 (RSS, Slack).

수집 KPI

• Captures per user per week (engagement)

• % users with 2+ surfaces (multi-surface adoption)

• Daily active capture rate

• Capture → first publish 시간 (UX)

7. Tier 2: 소화 (Digestion)

현재 상태 (이미 강함, 단 underleveraged)

✅ Concept ontology auto-extraction (doc_ontology job, 30-min throttle) ✅ Bundle graph analysis (themes, insights, edges via LLM) ✅ Embeddings (doc + chunk via OpenAI text-embedding-3-small) ✅ Concept relations (typed edges between concepts) ✅ Concept weights accumulation (cross-bundle) ✅ 3-provider AI failover (Anthropic → OpenAI → Gemini) ✅ /galaxy 시각화 (concept index visual reader) ✅ /canvas 시각화 (per-bundle graph visual) ✅ analysis_stale tracking (bundle freshness)

진짜 강점: Auto graph build

대부분 도구는 사용자가 organize. memory.wiki는 AI가 자동:

• 매 doc save → concept 자동 추출

• Bundle 만들면 → graph 자동 분석

• Concept relations 자동 build

• 모든 게 background

이게 product의 2nd moat. Lock-in mechanism.

강화 후보 (모두 keep)

A. Bundle 자동 생성 ⭐ (Tier 2 priority)

왜 중요: 현재는 manual. AI가 concept clustering으로 자동 제안.

구현:

• 사용자가 자연어 요청: "Project Acme 관련 묶어줘"

• AI가 concept_index + embeddings 검색

• Bundle draft 생성 + annotations

• 사용자 review/edit/save

또는 자동 suggest:

• "이 5 docs가 X 주제로 cluster됩니다. Bundle 만들까요?"

• 사용자 yes/no

Launch 전 build 가능성: High (3-4주). Magic UX.

B. Question-driven exploration ⭐

왜 중요: 사용자가 hub에 자연어 질문 → AI가 즉시 답변 + 관련 docs surface.

구현:

• memory.wiki/hub 페이지에 chat interface

• "내가 LLM memory에 대해 뭐 알고 있어?"

• AI가 embedding search + concept lookup

• 답변 + cited docs

Launch 전 build 가능성: High (2-3주). 가장 강력한 AI 활용.

C. Auto-summarization (digest)

왜 중요: Return user mechanic. 매일/매주/매월 review.

구현:

• Daily digest: "오늘 추가한 5 docs 요약"

• Weekly digest: "이번 주 thinking"

• Monthly review: "concept growth chart"

• Email 또는 in-app notification

Launch 전 build 가능성: Medium (3주).

D. Cross-bundle auto-link

왜 중요: Bundle A의 concept이 Bundle B에 등장 → 자동 cross-reference.

구현:

• Concept_relations 이미 있음 → activate

• Bundle UI에 "Related bundles" 섹션

• 자동 link via shared concepts

• Concept hub 자체에서 cross-bundle navigation

Launch 전 build 가능성: Medium (2주). 이미 backend 80% 있음.

E. Contradiction detection

왜 중요: Power user feature. 두 doc이 contradictory하면 surface.

구현:

• LLM이 concept-level contradiction detection

• "Doc A는 X라고 하는데 Doc B는 not X"

• 사용자에게 surface

• Resolve or reconcile prompt

Launch 전 build 가능성: Low (4주+). Niche but impressive.

F. Knowledge gap detection

왜 중요: "이 concept group에 X 비어있다" → active learning.

구현:

• Concept cluster 분석

• Gaps surface ("X에 대한 doc 없음")

• "Search/capture suggest"

Launch 전 build 가능성: Low (4주+). Niche.

G. Concept evolution tracking

왜 중요: "내 thinking이 6개월 전 vs 지금 어떻게 변했나"

구현:

• Concept snapshot 매월

• Diff 비교

• Personal growth narrative

• Visualization

Launch 전 build 가능성: Low (4주+). Long-term value, not launch critical.

소화 강화 우선순위 (Launch까지)

Launch 전 commit: 1, 2 (Bundle auto-gen + Question exploration). Launch 후 즉시: 3, 4 (Cross-link + Digest). Phase 3: 5, 6, 7 (Contradiction, Gap, Evolution).

소화 KPI

• Bundles created per user (manual + AI)

• AI bundle generation usage rate

• Concept index size per user

• Hub query frequency

• Return user rate (digest engagement)

8. Tier 3: 활용 (Utilization)

현재 상태 (이미 매우 강함)

✅ Doc URL — mdfy.app/<id> (markdown + frontmatter) ✅ Bundle URL Compact — mdfy.app/b/<id> (~95% 토큰 절약) ✅ Bundle URL Full — ?full=1 (모든 body inline) ✅ Hub URL Compact — mdfy.app/hub/<slug> (concept digest) ✅ Hub URL Since — ?since=date (incremental) ✅ /raw payload — for AIs / scrapers ✅ /embed — iframe-friendly ✅ /d/ — reader-only viewer ✅ MCP server (26 tools) — push + pull integration ✅ Visitor "Ask AI" — 공개 doc에 누구나 AI 질문 ✅ Real-time collaboration — Yjs CRDT ✅ Three permission roles — owner / editor / readonly ✅ Edit mode controls — owner / account / token / view / public

진짜 강점: 3 scope URL + Compact/Full + MCP

이게 product의 3rd moat. AI 회사들이 "delivery model"을 못 copy.

강화 후보 (모두 keep)

A. Direct LLM platform integration ⭐ (Tier 3 priority — Hyunsang님 vision)

왜 중요: "LLM의 default memory layer" vision의 직접 구현.

구현:

• "Add memory.wiki to ChatGPT" 버튼 → Custom GPT 자동 설치

• "Add memory.wiki to Claude" → MCP 자동 연결

• "Add memory.wiki to Cursor" → cursor rules 자동 설정

• 한 번 click → 모든 대화에 자동 컨텍스트

Launch 전 build 가능성: Medium (3-4주). Custom GPT부터 시작 가능.

전략적 가치: 매우 큼. Partnership 시작점.

B. Embedded chat with hub context ⭐

왜 중요: memory.wiki에서 직접 chat, hub 전체 자동 context. ChatGPT 안 가도 됨.

구현:

• mdfy.app에 chat UI

• 사용자 hub 자동 context (Compact)

• 3-provider failover

• Conversation history → 새 docs 생성 가능

Launch 전 build 가능성: High (2-3주). 기술 자산 이미 있음.

전략적 가치: Sticky engagement. ChatGPT/Claude lock-out도 가능.

C. Public hub sharing ⭐

왜 중요: 가장 viral mechanic. Substack-like "knowledge feed".

구현:

• "내 hub 공개" 옵션

• Public hub URL

• 다른 사람이 따라가기

• Concept-level subscribe

• "Following" feed

Launch 전 build 가능성: Medium (3주). 가장 viral.

D. AI-specific format adapters

왜 중요: Claude/ChatGPT/Cursor 각각 최적 format.

구현:

• Claude: XML tags

• ChatGPT: verbose markdown

• Cursor: code-context 강조

• Gemini: structured

URL parameter로 지정:

• ?format=claude

• ?format=cursor

Launch 전 build 가능성: Medium (2-3주). Quality moat.

E. Smart Hub digest

왜 중요: Context-aware digest. "지금 작업 중인 것" 인식.

구현:

• Recent 활동 분석

• Active concept cluster surface

• "Working context" vs "background" 구분

Launch 전 build 가능성: Low (4주+). UX polish.

F. Hub-to-hub merge

왜 중요: Team의 collective concept index.

구현:

• 두 hub 합치기

• Conflict resolution

• Permission inheritance

• Team tier 핵심

Launch 전 build 가능성: Low (4주+). Team feature, Phase 3.

G. Time travel

왜 중요: "3개월 전 내 hub vs 지금". Personal growth narrative.

구현:

• Hub snapshot 자동

• Diff 비교

• Timeline view

Launch 전 build 가능성: Low (4주+). Power user.

활용 강화 우선순위 (Launch까지)

Launch 전 commit: 1, 2, 3 (Platform integration + Embedded chat + Public hub). Launch 후 즉시: 4 (Format adapters). Phase 3: 5, 6, 7.

활용 KPI

• Hub URL deploys per user per week (활용 빈도)

• LLM platform integrations active (partnership)

• Public hub follows (viral signal)

• MCP tool usage rate

• Embedded chat engagement

9. Strategic Position

시장 자리

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Retrieval-focused      Delivery-focused
                (query systems)        (URL-based)
                     ↓                     ↓

Auto-extracted    Mem0, Letta            [없음 — gap]
                  OpenAI Memory
                  Google Memory Bank

User-authored     Notion (closed)        [memory.wiki] ⭐
                  Obsidian (local)

LLM-compiled      LLM Wiki (Karpathy)    [memory.wiki Phase 3]
                  (local Obsidian)        (cloud variant)

memory.wiki의 자리: User-authored + Delivery-focused + URL-based.

이 자리가 비어있음. memory.wiki가 차지.

5 Pillars of memory.wiki

1. URL as API — 어떤 AI든 fetch 가능, no SDK

2. Graph is product — concept ontology + relations

3. Multi-surface capture — 7+ surfaces from day one

4. AI-augmented authorship — user author + AI organize

5. Open Bundle Spec — standard formation

차별화 narrative (sharp)

vs Mem0/Letta

"They extract memory from your conversations. memory.wiki delivers your knowledge graph to any AI."

vs Karpathy LLM Wiki

"LLM Wiki is one local knowledge base, LLM-compiled. memory.wiki is doc + bundle + hub — scoped composition for cloud delivery."

vs Notion / Obsidian

"Notion locks knowledge in workspace. Obsidian locks in folder. memory.wiki ships knowledge as URL — readable by any AI."

vs ChatGPT Memory / OpenAI

"ChatGPT remembers about you, inside ChatGPT. memory.wiki is your knowledge, deployable to ChatGPT, Claude, Cursor — anywhere."

Honest positioning

Mem0와 Letta는 자동 추출에 훌륭합니다. Karpathy LLM Wiki는 local compilation에 훌륭합니다. Notion은 closed workspace에 훌륭합니다. memory.wiki는 그 사이의 layer입니다 — user-authored, AI-organized, URL-delivered, multi-AI compatible.

Strategic moat

3 layers of defensibility:

1. Multi-surface infrastructure — 7+ surfaces, day one shipped

2. Graph engine — concept ontology auto-build, accumulating

3. Delivery model — URL-based, any AI inherits

이 셋이 합쳐지면:

• 사용자 떠나기 어려움 (graph lock-in)

• 경쟁사 따라잡기 어려움 (multi-surface)

• AI 회사들이 copy 어려움 (delivery infrastructure)

10. Business Model

Pricing (v7 — 3-tier framework 반영)

Copy
Free
- 수집 unlimited (모든 surfaces)
- 소화 limited (월 50 docs concept extract, 3 bundles AI analyze)
- 활용 basic (Doc URL public only)
- Real-time collab (2 users)
- Community MCP server (read-only)

Pro $9/mo (개인) ⭐ 수집 + 소화 full
- 수집 unlimited
- 소화 unlimited (concept extract, bundle analyze)
- 활용 essential:
  * Private docs
  * Custom domain
  * Bundle Compact + Full
  * Hub URL (basic)
  * 10 AI-generated bundles/month
  * Embedded chat (limited)
- Tags, folders
- Version history
- Custom domain

Build $19/mo (Power user, AI builder) ⭐ 활용 full
- All Pro features
- 활용 unlimited:
  * Unlimited AI-generated bundles
  * AI-specific format adapters (Claude/GPT/Cursor)
  * Embedded chat unlimited
  * Public hub sharing
  * Hub URL custom domain
- API access (read/write)
- MCP server full (write enabled)
- Bundle versioning + snapshots
- Webhook integrations
- Voice capture (mobile)

Team $19/seat/mo
- All Build features
- Shared workspaces
- Shared bundles + hubs
- Hub-to-hub merge
- Permissions, audit log
- SSO (Google → SAML)
- Slack/Discord integration

Scale $499+/mo
- Public bundle marketplace
- Custom rate limits
- SLA, priority support
- Dedicated MCP infrastructure
- Multi-region

Enterprise (협의)
- Self-host option
- SAML SSO
- LLM Wiki integration support
- Custom audit/compliance
- Direct LLM platform partnership pricing

Pricing 의도 (3-tier driven)

Free → Pro $9

• Free에서 publishing 충분

• Pro upgrade 동기: AI-generated bundles 10개/월 (가장 강력) Private docs Custom domain 소화 unlimited

Pro → Build $19

• Bundle 무제한 AI generation

• AI-specific format adapters (quality moat)

• Public hub sharing (viral mechanic)

• API/MCP write access (developer)

Build → Team

• Shared workspaces

• Hub-to-hub merge

• Audit log

Team → Enterprise

• Self-host

• Platform partnership

• SOC 2

Unit Economics

Pro $9

• Stripe: $0.56

• Hosting: ~$0.50

• AI base (concept extract, bundle analyze): ~$1.00

• AI generation (avg 5/month): ~$0.30

• Embeddings: ~$0.05

• Margin: ~74%

Build $19

• Stripe: $0.85

• Hosting: ~$0.80

• Unlimited AI generation: ~$2.00

• Format adapters processing: ~$0.30

• API/MCP infra: ~$0.50

• Margin: ~71%

Margin이 v6보다 낮음 (AI cost 추가). 단 conversion이 더 강력해서 LTV 증가.

Revenue Projections (12개월)

Conservative

• 3,000 paid users

• ARPU $11

• $33K MRR / $400K ARR

Realistic

• 8,000 paid users

• ARPU $11

• $88K MRR / $1.0M ARR

Optimistic

• 18,000 paid users

• ARPU $12

• $216K MRR / $2.5M ARR

11. 16-Week Launch Roadmap

전체 schedule (Now → 2026년 8월 말)

Copy
Week 1-4    Brand + Foundation
Week 5-8    Tier 1 강화 (수집)
Week 9-11   Tier 2 강화 (소화)
Week 12-14  Tier 3 강화 (활용)
Week 15     Beta + Polish
Week 16     Public Launch

Week 1-4: Brand + Foundation

Week 1

• Brand identity 결정 (visual)

• About 페이지 v7 framing 적용

• Manifesto v7 작성

• memory.wiki landing page 활성화 (SEO 시작)

Week 2

• mdfy.app → memory.wiki migration 시작 (phased)

• Hero 새 카피 push

• Three pillars 새 본문 (수집/소화/활용)

• Bundle Spec v1.0 draft 공개 준비

Week 3

• 한국어 사이트 (선택, 또는 launch 후)

• Pricing 페이지 update (Build tier 활성화)

• Demo 영상 시나리오 작성 (3편)

• Press kit 준비

Week 4

• Investor 자산 (one-pager, deck draft)

• Beta tester 모집 시작 (50명 target)

• Manifesto post (Substack/blog)

Week 5-8: Tier 1 강화 (수집)

Week 5-6: iOS Share Sheet

• iOS Share Extension 개발

• Universal capture from any iOS app

• TestFlight 배포

• 기존 사용자 (Hyunsang님) 자체 테스트

Week 7-8: AI conversation auto-suggest

• Chrome extension upgrade

• ChatGPT/Claude 대화 monitor

• "Save this?" subtle prompt

• A/B test heuristics

Week 9-11: Tier 2 강화 (소화)

Week 9-10: Bundle 자동 생성

• AI bundle generation backend

• 자연어 요청 → bundle draft

• 사용자 review/edit UX

• Cross-bundle auto-link activate

Week 11: Question-driven exploration

• Hub chat interface

• Embeddings + concept lookup

• Cited answers UI

Week 12-14: Tier 3 강화 (활용)

Week 12: Embedded chat with hub context

• mdfy.app/chat UI

• Hub auto-context

• Conversation history → docs

Week 13: Public hub sharing

• "Make hub public" option

• Public hub URL

• Following feed (basic)

Week 14: Direct LLM platform integration (Custom GPT first)

• ChatGPT Custom GPT for memory.wiki

• One-click install

• Optional: Claude MCP guide, Cursor rules

Week 15: Beta + Polish

• 50명 beta tester 본격 운영

• 매일 피드백 처리

• Bug fix

• Onboarding 최적화

Week 16: Public Launch ⭐

Day 1-2: Final prep

• Demo 영상 3편 완성 (수집/소화/활용)

• Show HN 글 5개 후보 finalize

• Twitter thread 10 tweets

• Product Hunt 페이지

Day 3: Show HN

• 오전 9시 PST

• "Show HN: memory.wiki — your knowledge graph as a URL for any AI"

Day 4: Twitter + dev community

Day 5: Product Hunt

Day 6: Manifesto post + Bundle Spec announcement

Day 7: AI newsletters (Latent Space, AI Engineer, etc.)

12. Launch Strategy

Big Launch v7

3 narratives 동시에:

1. Product narrative

"memory.wiki — your knowledge graph as a URL for any AI." Multi-surface capture. AI auto-graph. Universal delivery.

2. Open standard narrative

"Bundle Spec v1.0 — open standard for AI-deployable knowledge." GitHub RFC. Reference implementation. Community formation.

3. Founder narrative

"I built memory.wiki in 5 months. Multi-surface, AI graph, MCP-ready. Now going full-time. Betting that markdown URLs become AI's universal interface."

Launch Day 채널 (Week 16)

Tuesday: Show HN

제목 후보:

1. "Show HN: memory.wiki — your knowledge graph as a URL for any AI"

2. "Show HN: I built a delivery-first AI memory layer (vs Mem0's extraction)"

3. "Show HN: Bundle Spec — open standard for AI-deployable knowledge"

4. "Show HN: memory.wiki + 26-tool MCP server for any LLM host"

5. "Show HN: A wiki for AI that any LLM can fetch as a URL"

저는 1 또는 5 추천. Sharp + accessible.

Wednesday: Twitter/X

• 10-tweet thread

• Demo GIFs (각 단계별)

• Karpathy LLM Wiki 보완재 framing

Thursday: Product Hunt

• "memory.wiki — Your knowledge graph as a URL for any AI"

Friday: Manifesto + Bundle Spec

• Manifesto post (1,800 단어)

• Bundle Spec announcement post (separate)

• AI newsletters

Pre-Launch Strategic Conversations

Week 12-15에 outreach:

Tier 1 (Partnership priority)

• Anthropic: MCP integration showcase

• Cursor: Memory layer integration (cursor rules)

• Continue (OSS): Reference implementation

• Cognition / Devin: Agent memory backbone

Tier 2 (Awareness)

• OpenAI: Custom GPT showcase

• Perplexity: Research memory

• Replit Agent: Memory infrastructure

Tier 3 (Community)

• LLM Wiki community: Spec feedback, complementary framing

• Karpathy: Spec 인지 (가능하면)

• Markdown community (Obsidian, HackMD): Awareness

Bundle Spec 발표

Pre-launch (Week 14-15)

• GitHub: github.com/raymindai/bundle-spec

• README + full spec

• Reference impl: memory.wiki

Launch day

• Spec announcement post

• HN/Twitter에 spec 링크

• LLM Wiki community에 보완재 framing

Post-launch

• RFC iteration

• Community contributions

• Other tools implement (Obsidian plugin 가능성)

KPI 목표 (v7)

Week 16 (Launch)

• 2,000-3,000 signup

• 500+ Chrome ext install

• 100-150 paid (mix Pro $9 + Build $19)

• $1,500-2,500 MRR

• 100+ Bundle 생성 (50%+ AI-generated)

• 50+ Hub URL 활성

• HN top 3

• Bundle Spec GitHub stars 200+

• 5-10 media mentions

Month 3 (Post-launch)

• 8,000-12,000 signup

• $8-15K MRR

• 500+ AI-generated bundles/week

• 100+ Public hubs

• 첫 LLM platform partnership 발표

Month 6

• 30,000-50,000 signup

• $25-40K MRR

• Voice capture launched (mobile)

• AI agent integrations 15+

• Team workspace beta

Month 9

• 100,000+ signup

• $50-80K MRR

• Bundle Spec v1.0 final

• 첫 enterprise pilot

• LLM Wiki Obsidian integration POC

Month 12

• $80-150K MRR

• 카테고리 인지 top 5

• Strategic conversations (Anthropic, OpenAI 등)

• Series A 가능 시점

13. 12-Month Outlook

Year 1 outcome 시나리오

Conservative

• $50-100K MRR

• 8,000-15,000 paying users

• Bundle Spec published, 200+ stars

• Voice capture launched

• 첫 partnership announcement

Realistic

• $100-200K MRR

• 15,000-25,000 paying users

• Bundle Spec v1.0 final

• LLM Wiki integration

• 첫 enterprise pilot

• Series A optional

Optimistic

• $250-400K MRR

• 30,000+ paying users

• Bundle Spec adoption (3+ tools)

• 첫 LLM platform default integration

• Series A active

Year 2

Conservative

• $500K-1.5M ARR

• Sustainable indie + spec community

• 강한 카테고리 brand

Realistic

• $2-5M ARR

• Bundle Spec open standard

• 첫 enterprise customers

• 외부 자본 검토 또는 받음

Optimistic

• $5-15M ARR

• C2PA-style consortium

• Strategic acquisition 제안 ($50-200M)

• Series A ($30-100M valuation)

Year 3

Conservative

• $1.5-5M ARR sustainable

Realistic

• $10-25M ARR

• Bundle Spec industry standard

• Acquisition ($100-300M)

• 또는 Series B

Optimistic

• $30M+ ARR

• LLM platform default integration

• Acquisition $200M-1B range

• Series B 가능

Exit Scenarios

Strategic Acquisition Candidates

• Anthropic: Memory layer for Claude/MCP ecosystem

• OpenAI: Personal memory infrastructure

• GitHub/Microsoft: Knowledge hub for Copilot

• Notion: AI memory acquisition (counter-positioning)

• Cursor / Cognition: Agent memory backbone

• Atlassian / Linear: Team docs + memory

Sustainable Indie

• $5M+ MRR

• Spec community

• Personal asset + freedom

Standards Body

• Bundle Spec → industry standard

• Long-term shaping (W3C-like)

14. Risk Register

Risk 1: 거인들 진입 (확률 60%, impact 매우 큼)

시나리오

• Anthropic memory layer

• OpenAI Memory 확장

• Google Memory Bank

Mitigation

• 16주 launch (first-mover advantage 빠르게)

• Bundle Spec 발표 (표준 차지)

• Multi-LLM agnostic (거인은 자기 ecosystem만)

• Delivery model (closed system 따라잡기 어려움)

• LLM Wiki community 동맹

Risk 2: Build scope creep (확률 50%, impact 매우 큼)

시나리오

• 3 tier 각각 enhancement 모두 build 시도

• Launch 16주 → 24주 → 무한 연기

Mitigation

• 각 tier launch 전 commit features 명확 (1, 2번만)

• 나머지는 Phase 3로

• 매주 progress review

• Week 8, 12, 14 reality checks

Risk 3: 첫 beta tester 모집 어려움 (확률 15%, impact 큼)

Mitigation

• 채널 5개 다각화 (Discord, HN, Twitter, LLM Wiki community, Markdown community)

• mdfy.app 이미 사용 가능 (try first, no waitlist)

• Manifesto post로 inbound

Risk 4: Graph 개념 사용자 이해 못 함 (확률 40%, impact 중간)

시나리오

• "Concept graph"가 abstract

• 사용자가 가치 못 느낌

Mitigation

• /galaxy 시각화로 즉시 see

• Demo 영상에서 use case

• "그래프 자동" UX (사용자가 build할 필요 없음)

• Question-driven exploration로 즉시 utility

Risk 5: Bundle Spec adoption 안 됨 (확률 50%, impact 중간)

Mitigation

• memory.wiki가 reference impl

• LLM Wiki community 협업

• Spec 단순함 (CommonMark + YAML)

• Format adapters로 immediate value

Risk 6: AI cost spiral (확률 30%, impact 중간) ⭐ NEW

시나리오

• Free tier 사용자가 AI feature 남용

• AI generation cost가 revenue 초과

Mitigation

• Rate limits (월 50 docs concept extract for Free)

• AI generation paywall (Pro 10/month)

• 3-provider failover (cost optimize)

• Embedding cache (embedding_source_hash)

• Concept extraction throttle (30 min per doc)

Risk 7: Solo burnout (확률 50%, impact 매우 큼)

Mitigation

• 16주 timeline (8주보다 sustainable)

• 매주 1일 OFF

• Week 8, 12 reality check

• Month 3+ 자동화

• Month 6+ 외주 (콘텐츠, 카피)

Risk 8: LLM platform 미협력 (확률 40%, impact 큼) ⭐ NEW

시나리오

• "Default memory layer" vision

• 단 Anthropic/OpenAI가 자기 solution 우선

• Partnership 거부

Mitigation

• Plan B: bottom-up (사용자 → request 압박)

• Custom GPT, MCP는 platform 동의 필요 없음

• 사용자 base 충분히 크면 platform이 협력 동기

• 단기에는 ChatGPT Custom GPT부터

Risk 9: Brand rename confusion (확률 20%, impact 중간) ⭐ NEW

시나리오

• mdfy.app → memory.wiki migration

• 기존 사용자 confusion

• SEO 손실

Mitigation

• Phased migration (Week 2 시작)

• mdfy.app → memory.wiki 자동 redirect (영구)

• Email 사용자 알림

• Brand story로 narrative 만들기

15. Brand Identity (TBD)

결정된 것

• Name: memory.wiki (final, 도메인 확보)

• Tone: 진지함 + 따뜻함 (knowledge + craft)

• Voice: Authoritative + accessible

TBD (Visual)

이전에 mori.wiki 시절 결정한 "90% 숲 + 10% 해골 + pixel art"는 mori (memento mori) 의미와 align한 것. memory.wiki로 변경되면서 reconsider 필요.

Visual 옵션

A: Pixel art forest + skull keep (narrative 조정)

"Memory like a forest. The skull is the reminder."

B: 새 visual identity (memory에 fit)

Brain, neural network, library 등 motif

C: Minimalist (no specific motif)

단순 wordmark, monogram

D: Decide at launch -2 weeks (doc 전략)

추천: D (Launch 임박 시 결정)

doc의 strategy 따라 launch 2주 전 finalize. 그때까지 wordmark만 사용.

Color palette 후보

Tone 1: 깊고 진지함

• Deep blue + cream

• Inkwell knowledge feel

Tone 2: 자연 + 지식

• Forest greens + bone cream (mori legacy)

• Growth + memento

Tone 3: AI 시대 modern

• Charcoal + electric blue

• Tech but human

Launch 2주 전 결정.

16. Decisions Log (v7)

v6 결정사항 유지

v7 신규 결정사항

Tier별 launch features (commit)

수집 (Tier 1)

• ✅ iOS Share Sheet

• ✅ AI conversation auto-suggest

• ⏳ Voice capture (POC if time)

• 🔜 Email forward (post-launch)

• 🔜 RSS sync (Phase 3)

• 🔜 Slack/Discord (Phase 3)

소화 (Tier 2)

• ✅ Bundle 자동 생성

• ✅ Question-driven exploration

• ✅ Cross-bundle auto-link

• 🔜 Auto-summarization digest (post-launch)

• 🔜 Contradiction detection (Phase 3)

• 🔜 Knowledge gap detection (Phase 3)

• 🔜 Concept evolution (Phase 3)

활용 (Tier 3)

• ✅ Direct LLM platform integration (Custom GPT first)

• ✅ Embedded chat with hub context

• ✅ Public hub sharing

• 🔜 AI-specific format adapters (post-launch)

• 🔜 Smart Hub digest (Phase 3)

• 🔜 Hub-to-hub merge (Phase 3)

• 🔜 Time travel (Phase 3)

Mantra

매일 self-check

"이 feature 없으면 launch 못 하는가?"NO → Phase 3 list로 미룸

"3-tier 어느 부분 강화 중인가?"수집 / 소화 / 활용 각각 1개씩 commit

"Graph가 작동하는가?"Graph = product. 깨지면 안 됨.

"URL = API thesis가 살아있는가?"모든 결정의 anchor

"이게 movement를 만드는가?"Standard-setting ambition.

한 줄

memory.wiki — your knowledge graph as a URL for any AI. 수집은 multi-surface로. 소화는 AI graph로. 활용은 어떤 AI든 URL로. 16주 안에 launch한다. Markdown URLs become the universal AI memory interface.

부록: 관련 문서

작성 완료된 문서

1. mdfy-master-business-plan.md — v4 (deprecated)

2. mdfy-direction-v5.md — v5 (deprecated)

3. mdfy-direction-v6.md — v6 (deprecated)

4. mdfy-manifesto-v6-en.md — v6 manifesto (needs v7 rewrite)

5. mdfy-manifesto-v6-ko.md — v6 한국어 manifesto

6. mdfy-three-pillars-v6.md — v6 pillars

7. mdfy-bundle-spec-v1.md — Bundle Spec (v7 update 필요)

8. mdfy-claude-code-handoff.md — interim site (deprecated)

9. mdfy-direction-v7.md — 이 문서 (v7 FINAL)

다음 작성 필요

• Manifesto v7 (memory.wiki + 3-tier + graph thesis 반영) ← 다음 task

• Bundle Spec v2 (memory_bundle field 변경)

• Three pillars v7 (수집/소화/활용)

• Show HN 글 5개 후보

• Demo 영상 시나리오 3편

Ground truth references

• OVERVIEW.md: https://mdfy.app/bmZYfZez

• HOW-IT-WORKS.md: https://mdfy.app/KRKz_MD-

문서 작성: 2026-05-20Brand: memory.wiki (final)Launch: 2026년 8월 말 (16주)Built by: Hyunsang at Raymind.AIExisting: github.com/raymindai/ (mdfy → memory.wiki migration) Contact: hi@raymind.ai